Effect of average happiness for Twitter on the Dow Jones Industrial Average return volatility

Abstract

The stock market is well known for its volatility and many models are proposed to capture the volatility. Volatility is naturally unobservable and the absolute values of the returns work as the realized volatility. The Dow Jones Industrial Average is the study object and the models used are generalized autoregressive conditional heteroskedasticity (GARCH) models with different extensions. The unique extension in this study is to add happiness data into the model and check whether it helps to better capture the volatility and improve the forecasting accuracy. The happiness data is extracted from Twitter and it is an index to show people's happiness level based on their online expressions. The one day lagged happiness data is also used as one extension to the models. The leverage effects and the heavy tails problems are also addressed in this study, EGARCH models and GJR-GARCH models with other error distributions such as student's T distribution are used to deal with these specific problems. The forecasting performance of these models is checked and we find out that the happiness data does help to better capture the volatility. However, the forecasting accuracy of the models with happiness data is not statistically different compared to the models without happiness data. This illustrates that the happiness data does not help to improve the forecasting performance.^